Part I - Income and Health. What does the data say?

by Olubunmi Adejimi

Introduction

This is a dataset of the Population growth, fertility, life expectancy and mortality for countries all over the world collected from https://data.un.org/. This data contains the following information:

  • Infant mortality for both sexes (per 1,000 live births),
  • Life expectancy at birth for both sexes (years),
  • Life expectancy at birth for females (years),
  • Life expectancy at birth for males (years),
  • Maternal mortality ratio (deaths per 100,000 population),
  • Population annual rate of increase (percent),
  • Total fertility rate (children per women)

A second dataset from https://databank.worldbank.org/source/world-development-indicators containing the Gross National Income (GNI) per capita for the years 2010, 2015 and 2020, is combined with the previous dataset.

These data are combined with a GeoJSON file collected from https://geojson-maps.ash.ms/, to have a more detailed dataset for a robust analyses.

Preliminary Wrangling

Load in your dataset and describe its properties through the questions below. Try and motivate your exploration goals through this section.

What is the structure of your dataset?

The datset has 447 rows and 13 columns.

What is/are the main feature(s) of interest in your dataset?

The main features of interest in the dataset are:

  • Infant mortality for both sexes (per 1,000 live births),
  • Life expectancy at birth for both sexes (years),
  • Life expectancy at birth for females (years),
  • Life expectancy at birth for males (years),
  • Maternal mortality ratio (deaths per 100,000 population),
  • Population annual rate of increase (percent),
  • Total fertility rate (children per women)
  • GNI per capita

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

These fetaures; Country, continent, region, subregion, economy, 'Income level, andYear` will support my investigation.

Univariate Exploration

Q: What is the distribution of infant mortality

Q: What is the distribution of maternal mortality

Q: What is the most probable life expectancy of countries?

Q: What is the most probable rate of annual population increase?

Q: What is the most probable GNI per capita in USD for people to earn globally

Q: How many countries belong to the different each income level?

Bivariate Exploration

Q: What is the distribution of variables of interest in each continent

Q: How have the variables of interest changed globally over 10 years?

Q: Is there a difference in variables for different continents?

Q: Has the number of countries changed in each income level over a deccade?

Q: How many countries in each continent belongs to the different economic class?

Q: How many countries in each subregion belongs to each economic class?

It is obseerved that developed countries have the lowest annual rate of population increase, while the least developed countries have the highest annual rate of population increase. Africa has the highest fertility rate with at about 4.5 children per woman followed closely by Oceania, and Europe has the lowest with <1 child per woman. Fertility rate has also dropped globally between 2010 and 2020. Infant mortality rate has dropped by ~5 points between 2010 and 2020, and Africa has the highest infant mortality rate.

The least developed nations are predominantly African and Asian countries. Most developed countries are European and most developing countries are Asian. Easter African has more least developed countries, than other African regions. Northern Europe, Northern America and Autralia and New Zealand are the only subregions that are considered strictly developed.

Multivariate Exploration

Q: How has the parameter of interest changed over 10 years in different continents?

Q: How has the parameter of interest changed over 10 years for each income level

Q: How different are the average of the variables of interest in different continents in different years

Q: Is there any correlation between numerical variables?

Q: What is the relationship between infant mortality and life expectancy in different continents?

Q: What is the relationship between maternal mortality and infant mortality in different income levels?

Q: What is the relationship between fertility rate and infant mortality in different income levels

Q: What is the relationship between maternal mortality and infant mortality in different continents?

Q: What has been the relationship between income level and the other variables over the years?

Q: What has been the relationship between GNI per capita and other variables in differents regions and income levels

Q: Does fertility rate have any impact of the growth of the population

Conclusions

Sequel to exploring this dataset, we can conclude the following;

  • More countries are categorized as High incomeand the least number of countries as low income.
  • More countries have lower infant and maternal mortality rate, population increase rate and higher life expectancy.
  • Developed countries have the lowest rate of population growth, which appears to be related to the low fertility rate in developed countries. Africa has the highest fertility rate and annual population increase rate and is predominantly a low income economy.
  • There has been a drop in the annual rate of population increase between 2010 and 2020, with Asia experiencing the most reduction in rate of population increase and South America has seen an uptrend in the rate of population increase.
  • There is a strong correlation between maternal mortality and infant mortality, indicating that a child whose mother dies is likely to survive, especially in least developed economies like Africa.
  • While the African continent and low income economies show poor data in terms of mortalities and life expectancies, they also have the highest improvements in these aspects. This is an indication of improved health and healthcare.
  • The high fertility rate in the African continent and 'least developed economies, indicates that the population is younger which can be beneficial towards improvement the economy and overall livelihood and health of its citizens.

Further Work

  • Explore in more details relating to subregions, to understand the changes on a smaller scale
  • Explore applying this data for forecasting future trends
  • Is it possible to predict the economic class of a country using this dataset?